Cost-sensitive algorithms are also used to address class imbalance. Unlike resampling methods, which are generally applied as a pre-processing step to data, cost-sensitive algorithms modify the model itself by assigning a higher cost to misclassified examples. Cost-sensitive methods have gained considerable interest in deep learning because they are more efficient than resampling methods, and recently deep learning-specific methods have been developed. Three leading cost-sensitive algorithms are: the Asymmetric Loss (ASL) [1]}, the Focal loss [2]}, and the Label-Distribution-Aware Margin loss (LDAM) [3]}. ASL assigns different costs to positive and negative samples through a targeted decay rate. The focal loss assigns higher losses to hard examples (which presumably are from the minority class) and lower relative costs to easy examples (which presumably are from classes with a greater number of samples). LDAM encourages higher margins for minority classes and also employs a class reweighting strategy that is deferred until later training epochs.
